[1]} and [2]} have independently developed the first set of neural abstractive summarizers implemented using attentional encoder-decoder RNNs (or sequence-to-sequence models), which are originally developed for machine translation [3]}, [4]}. In these summarizers, a RNN decoder generates a summary of input \(\mathcal {D}\)  which is encoded by an attention-based encoder to ensure that the decoder focuses on the appropriate input words at each step of generation:
\(P_r(Y | \mathcal {D}) = \prod _{i=1}^{N} P_r(y_i | y_{1},...,y_{i-1}, \mathcal {D}; \theta ).\) 
